Overview

Dataset statistics

Number of variables18
Number of observations95467
Missing cells3
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 MiB
Average record size in memory152.0 B

Variable types

Numeric9
Categorical9

Dataset

DescriptionDashboard of dataset clientes diciembre
CreatorJose Angel Carballo Sanchez
AuthorMiguel Moreno
URL

Variable descriptions

edadEdad de los clientes.
facturacionDinero que pagan los clientes al mes.
antiguedadFecha de alta del cliente.
provinciaProvincia de los clientes.
num_lineasNumero de lineas moviles contratadas.
num_lineas_impagoNumero de lineas en impago.
incidencia SI = el cliente ha tenido alguna incidencia o reclamacion.
conexionTipo de conexion de internet del cliente.
vel_conexionVelocidad de conexion de internet.
TVTipo de paquete de tv contratado por el cliente.
num_llamad_entNumero de llamadas entrantes de todas sus lineas.
num_llamad_salNumero de llamadas salientes de todas sus lineas.
mb_datosMb de los datos consumidos en todas sus lineas.
seg_llamad_entSegundos consumidos en llamadas entrantes.
seg_llamad_salSegundos consumidos en llamadas salientes.
financiacionSI = el cliente tiene financiado algun terminal.
imp_financEl dinero mensual que paga por los terminales financiados.
descuentosSI = el cliente tiene activado algun descuento.

Alerts

antiguedad has a high cardinality: 95171 distinct values High cardinality
conexion is highly correlated with vel_conexionHigh correlation
vel_conexion is highly correlated with conexionHigh correlation
antiguedad is uniformly distributed Uniform
facturacion has unique values Unique
imp_financ has 89095 (93.3%) zeros Zeros

Reproduction

Analysis started2022-05-04 15:39:30.939340
Analysis finished2022-05-04 15:40:12.424394
Duration41.49 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

edad
Real number (ℝ≥0)

Edad de los clientes.

Distinct68
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.77937926
Minimum18
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-05-04T17:40:12.705193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q132
median49
Q367
95-th percentile82
Maximum85
Range67
Interquartile range (IQR)35

Descriptive statistics

Standard deviation19.83296348
Coefficient of variation (CV)0.3984172518
Kurtosis-1.228142561
Mean49.77937926
Median Absolute Deviation (MAD)17
Skewness0.1165115285
Sum4752288
Variance393.3464405
MonotonicityNot monotonic
2022-05-04T17:40:12.992235image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=68)
ValueCountFrequency (%)
371721
 
1.8%
201671
 
1.8%
271653
 
1.7%
261644
 
1.7%
231641
 
1.7%
391641
 
1.7%
241639
 
1.7%
321637
 
1.7%
381635
 
1.7%
211614
 
1.7%
Other values (58)78971
82.7%
ValueCountFrequency (%)
181614
1.7%
191543
1.6%
201671
1.8%
211614
1.7%
221571
1.6%
ValueCountFrequency (%)
851260
1.3%
841235
1.3%
831268
1.3%
821286
1.3%
811326
1.4%

facturacion
Real number (ℝ≥0)

UNIQUE

Dinero que pagan los clientes al mes.

Distinct95467
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean207.3929122
Minimum15.00043941
Maximum399.9984328
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-05-04T17:40:13.306161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum15.00043941
5-th percentile34.13135858
Q1111.383822
median206.808431
Q3304.4365988
95-th percentile380.7972197
Maximum399.9984328
Range384.9979934
Interquartile range (IQR)193.0527768

Descriptive statistics

Standard deviation111.3434907
Coefficient of variation (CV)0.536872208
Kurtosis-1.204851845
Mean207.3929122
Median Absolute Deviation (MAD)96.45399205
Skewness0.005444336027
Sum19799179.15
Variance12397.37292
MonotonicityNot monotonic
2022-05-04T17:40:13.626116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=80)
ValueCountFrequency (%)
216.02810891
 
< 0.1%
107.57847791
 
< 0.1%
291.0646711
 
< 0.1%
224.80574571
 
< 0.1%
227.62622441
 
< 0.1%
65.355760771
 
< 0.1%
244.34680431
 
< 0.1%
316.57035751
 
< 0.1%
16.66126411
 
< 0.1%
130.65412761
 
< 0.1%
Other values (95457)95457
> 99.9%
ValueCountFrequency (%)
15.000439411
< 0.1%
15.000760021
< 0.1%
15.01340851
< 0.1%
15.017077411
< 0.1%
15.020459721
< 0.1%
ValueCountFrequency (%)
399.99843281
< 0.1%
399.99744321
< 0.1%
399.99158261
< 0.1%
399.98529741
< 0.1%
399.98357311
< 0.1%

antiguedad
Categorical

HIGH CARDINALITY
UNIFORM

Fecha de alta del cliente.

Distinct95171
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1995-07-14 08:11:00
 
2
2018-09-10 01:26:00
 
2
2019-06-13 05:16:00
 
2
2019-11-29 22:59:00
 
2
2003-08-22 05:27:00
 
2
Other values (95166)
95457 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94875 ?
Unique (%)99.4%

Sample

1st row2018-11-23 08:48:00
2nd row2017-08-22 03:19:00
3rd row2001-12-27 13:50:00
4th row2015-08-08 10:53:00
5th row1997-08-29 02:19:00

Common Values

ValueCountFrequency (%)
1995-07-14 08:11:002
 
< 0.1%
2018-09-10 01:26:002
 
< 0.1%
2019-06-13 05:16:002
 
< 0.1%
2019-11-29 22:59:002
 
< 0.1%
2003-08-22 05:27:002
 
< 0.1%
2008-05-03 01:45:002
 
< 0.1%
2007-03-01 08:11:002
 
< 0.1%
2019-08-08 12:39:002
 
< 0.1%
2011-11-29 07:04:002
 
< 0.1%
2013-01-25 02:44:002
 
< 0.1%
Other values (95161)95447
> 99.9%

Length

2022-05-04T17:40:13.895200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01:44:00100
 
0.1%
06:01:0096
 
0.1%
18:27:0091
 
< 0.1%
16:18:0091
 
< 0.1%
21:27:0090
 
< 0.1%
01:08:0089
 
< 0.1%
03:33:0089
 
< 0.1%
14:51:0089
 
< 0.1%
03:24:0088
 
< 0.1%
15:36:0088
 
< 0.1%
Other values (10561)190023
99.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

provincia
Categorical

Provincia de los clientes.

Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Zaragoza
 
1991
Navarra
 
1986
Málaga
 
1973
Valencia
 
1972
Asturias
 
1972
Other values (45)
85573 

Length

Max length22
Median length7
Mean length7.604051662
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLa Rioja
2nd rowVizcaya
3rd rowAlbacete
4th rowLugo
5th rowTarragona

Common Values

ValueCountFrequency (%)
Zaragoza1991
 
2.1%
Navarra1986
 
2.1%
Málaga1973
 
2.1%
Valencia1972
 
2.1%
Asturias1972
 
2.1%
Murcia1967
 
2.1%
Orense1958
 
2.1%
Alicante1954
 
2.0%
Córdoba1949
 
2.0%
Cáceres1945
 
2.0%
Other values (40)75800
79.4%

Length

2022-05-04T17:40:14.107488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
la3798
 
3.4%
zaragoza1991
 
1.8%
navarra1986
 
1.8%
málaga1973
 
1.8%
valencia1972
 
1.8%
asturias1972
 
1.8%
murcia1967
 
1.8%
orense1958
 
1.8%
alicante1954
 
1.8%
córdoba1949
 
1.8%
Other values (47)89175
80.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

num_lineas
Real number (ℝ≥0)

Numero de lineas moviles contratadas.

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.559261315
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-05-04T17:40:14.303823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q34
95-th percentile5
Maximum39
Range38
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.095542068
Coefficient of variation (CV)0.3078004031
Kurtosis12.83271021
Mean3.559261315
Median Absolute Deviation (MAD)1
Skewness0.2089489852
Sum339792
Variance1.200212422
MonotonicityNot monotonic
2022-05-04T17:40:14.482883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
330013
31.4%
426619
27.9%
522794
23.9%
213186
13.8%
12852
 
3.0%
181
 
< 0.1%
391
 
< 0.1%
261
 
< 0.1%
ValueCountFrequency (%)
12852
 
3.0%
213186
13.8%
330013
31.4%
426619
27.9%
522794
23.9%
ValueCountFrequency (%)
391
 
< 0.1%
261
 
< 0.1%
181
 
< 0.1%
522794
23.9%
426619
27.9%

num_lineas_impago
Categorical

Numero de lineas en impago.

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
90738 
4.0
 
1206
1.0
 
1179
2.0
 
1174
3.0
 
1170

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row2.0

Common Values

ValueCountFrequency (%)
0.090738
95.0%
4.01206
 
1.3%
1.01179
 
1.2%
2.01174
 
1.2%
3.01170
 
1.2%

Length

2022-05-04T17:40:14.712162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-04T17:40:14.868630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.090738
95.0%
4.01206
 
1.3%
1.01179
 
1.2%
2.01174
 
1.2%
3.01170
 
1.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

incidencia
Categorical

SI = el cliente ha tenido alguna incidencia o reclamacion.

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
NO
91893 
SI
 
3574

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO
2nd rowNO
3rd rowNO
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
NO91893
96.3%
SI3574
 
3.7%

Length

2022-05-04T17:40:15.211370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-04T17:40:15.346081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
no91893
96.3%
si3574
 
3.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

num_llamad_ent
Real number (ℝ≥0)

Numero de llamadas entrantes de todas sus lineas.

Distinct251
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.8156326
Minimum0
Maximum250
Zeros351
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-05-04T17:40:15.543120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q162
median124
Q3188
95-th percentile238
Maximum250
Range250
Interquartile range (IQR)126

Descriptive statistics

Standard deviation72.49233812
Coefficient of variation (CV)0.5807953426
Kurtosis-1.198935036
Mean124.8156326
Median Absolute Deviation (MAD)63
Skewness0.003237366258
Sum11915774
Variance5255.139086
MonotonicityNot monotonic
2022-05-04T17:40:15.870546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=80)
ValueCountFrequency (%)
43439
 
0.5%
93426
 
0.4%
114424
 
0.4%
3421
 
0.4%
11417
 
0.4%
108417
 
0.4%
15416
 
0.4%
80416
 
0.4%
137414
 
0.4%
5414
 
0.4%
Other values (241)91263
95.6%
ValueCountFrequency (%)
0351
0.4%
1378
0.4%
2378
0.4%
3421
0.4%
4400
0.4%
ValueCountFrequency (%)
250400
0.4%
249369
0.4%
248379
0.4%
247348
0.4%
246390
0.4%

num_llamad_sal
Real number (ℝ≥0)

Numero de llamadas salientes de todas sus lineas.

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.02276179
Minimum0
Maximum100
Zeros943
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-05-04T17:40:16.188021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q125
median50
Q375
95-th percentile95
Maximum100
Range100
Interquartile range (IQR)50

Descriptive statistics

Standard deviation29.11990386
Coefficient of variation (CV)0.5821330694
Kurtosis-1.198725458
Mean50.02276179
Median Absolute Deviation (MAD)25
Skewness-0.003468829953
Sum4775523
Variance847.9688008
MonotonicityNot monotonic
2022-05-04T17:40:16.507666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=80)
ValueCountFrequency (%)
771041
 
1.1%
911012
 
1.1%
611010
 
1.1%
541003
 
1.1%
11999
 
1.0%
40998
 
1.0%
39998
 
1.0%
71994
 
1.0%
20993
 
1.0%
4989
 
1.0%
Other values (91)85430
89.5%
ValueCountFrequency (%)
0943
1.0%
1932
1.0%
2939
1.0%
3948
1.0%
4989
1.0%
ValueCountFrequency (%)
100978
1.0%
99882
0.9%
98943
1.0%
97938
1.0%
96895
0.9%

mb_datos
Real number (ℝ≥0)

Mb de los datos consumidos en todas sus lineas.

Distinct24456
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12489.7959
Minimum0
Maximum25000
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-05-04T17:40:16.819625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1217
Q16177.5
median12466
Q318785.5
95-th percentile23749.7
Maximum25000
Range25000
Interquartile range (IQR)12608

Descriptive statistics

Standard deviation7239.421267
Coefficient of variation (CV)0.5796268671
Kurtosis-1.207194636
Mean12489.7959
Median Absolute Deviation (MAD)6304
Skewness-0.0002297470747
Sum1192363345
Variance52409220.28
MonotonicityNot monotonic
2022-05-04T17:40:17.141540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=80)
ValueCountFrequency (%)
1109816
 
< 0.1%
595815
 
< 0.1%
906414
 
< 0.1%
2346713
 
< 0.1%
460313
 
< 0.1%
1040613
 
< 0.1%
905412
 
< 0.1%
1686912
 
< 0.1%
1299512
 
< 0.1%
979912
 
< 0.1%
Other values (24446)95335
99.9%
ValueCountFrequency (%)
01
 
< 0.1%
18
< 0.1%
28
< 0.1%
34
< 0.1%
46
< 0.1%
ValueCountFrequency (%)
250003
< 0.1%
249995
< 0.1%
249982
 
< 0.1%
249974
< 0.1%
249964
< 0.1%

seg_llamad_ent
Real number (ℝ≥0)

Segundos consumidos en llamadas entrantes.

Distinct19829
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9945.152849
Minimum0
Maximum20000
Zeros356
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-05-04T17:40:17.458493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile935
Q14951
median9923
Q314948.5
95-th percentile18973
Maximum20000
Range20000
Interquartile range (IQR)9997.5

Descriptive statistics

Standard deviation5784.158514
Coefficient of variation (CV)0.5816057935
Kurtosis-1.199947317
Mean9945.152849
Median Absolute Deviation (MAD)4996
Skewness0.005887958733
Sum949433907
Variance33456489.71
MonotonicityNot monotonic
2022-05-04T17:40:17.779231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=80)
ValueCountFrequency (%)
0356
 
0.4%
388615
 
< 0.1%
695914
 
< 0.1%
1443314
 
< 0.1%
772714
 
< 0.1%
1030814
 
< 0.1%
1845014
 
< 0.1%
1370914
 
< 0.1%
472814
 
< 0.1%
114514
 
< 0.1%
Other values (19819)94984
99.5%
ValueCountFrequency (%)
0356
0.4%
17
 
< 0.1%
23
 
< 0.1%
36
 
< 0.1%
45
 
< 0.1%
ValueCountFrequency (%)
200006
< 0.1%
199989
< 0.1%
199977
< 0.1%
199966
< 0.1%
199954
< 0.1%

seg_llamad_sal
Real number (ℝ≥0)

Segundos consumidos en llamadas salientes.

Distinct19821
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9929.715221
Minimum0
Maximum20000
Zeros949
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-05-04T17:40:18.110148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile813.3
Q14910
median9922
Q314961
95-th percentile19005
Maximum20000
Range20000
Interquartile range (IQR)10051

Descriptive statistics

Standard deviation5819.207033
Coefficient of variation (CV)0.5860396701
Kurtosis-1.194811984
Mean9929.715221
Median Absolute Deviation (MAD)5025
Skewness-0.002391337726
Sum947960123
Variance33863170.49
MonotonicityNot monotonic
2022-05-04T17:40:18.433041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=80)
ValueCountFrequency (%)
0949
 
1.0%
1096816
 
< 0.1%
1844315
 
< 0.1%
1736014
 
< 0.1%
176314
 
< 0.1%
1532814
 
< 0.1%
1806014
 
< 0.1%
501014
 
< 0.1%
1988613
 
< 0.1%
1103213
 
< 0.1%
Other values (19811)94391
98.9%
ValueCountFrequency (%)
0949
1.0%
17
 
< 0.1%
28
 
< 0.1%
35
 
< 0.1%
43
 
< 0.1%
ValueCountFrequency (%)
200005
< 0.1%
199995
< 0.1%
199984
< 0.1%
199977
< 0.1%
199968
< 0.1%

conexion
Categorical

HIGH CORRELATION

Tipo de conexion de internet del cliente.

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
ADSL
48630 
FIBRA
46837 

Length

Max length5
Median length4
Mean length4.49060932
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFIBRA
2nd rowFIBRA
3rd rowADSL
4th rowFIBRA
5th rowADSL

Common Values

ValueCountFrequency (%)
ADSL48630
50.9%
FIBRA46837
49.1%

Length

2022-05-04T17:40:18.704143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-04T17:40:18.996198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
adsl48630
50.9%
fibra46837
49.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

vel_conexion
Categorical

HIGH CORRELATION

Velocidad de conexion de internet.

Distinct14
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size1.5 MiB
200MB
9675 
600MB
9622 
50MB
9474 
300MB
9460 
100MB
9332 
Other values (9)
47901 

Length

Max length5
Median length4
Mean length4.398935724
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row50MB
2nd row600MB
3rd row35MB
4th row200MB
5th row10MB

Common Values

ValueCountFrequency (%)
200MB9675
10.1%
600MB9622
10.1%
50MB9474
9.9%
300MB9460
9.9%
100MB9332
9.8%
20MB8113
8.5%
25MB8112
8.5%
10MB7969
8.3%
30MB7948
8.3%
35MB7947
8.3%
Other values (4)7812
8.2%

Length

2022-05-04T17:40:19.143372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
200mb9675
10.1%
600mb9622
10.1%
50mb9474
9.9%
300mb9460
9.9%
100mb9332
9.8%
20mb8113
8.5%
25mb8112
8.5%
10mb7969
8.3%
30mb7948
8.3%
35mb7947
8.3%
Other values (4)7812
8.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TV
Categorical

Tipo de paquete de tv contratado por el cliente.

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
tv-futbol
49634 
tv-familiar
32746 
tv-total
13087 

Length

Max length11
Median length9
Mean length9.548933139
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtv-futbol
2nd rowtv-futbol
3rd rowtv-futbol
4th rowtv-familiar
5th rowtv-futbol

Common Values

ValueCountFrequency (%)
tv-futbol49634
52.0%
tv-familiar32746
34.3%
tv-total13087
 
13.7%

Length

2022-05-04T17:40:19.352315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-04T17:40:19.489390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
tv-futbol49634
52.0%
tv-familiar32746
34.3%
tv-total13087
 
13.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

financiacion
Categorical

SI = el cliente tiene financiado algun terminal.

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
NO
89095 
SI
 
6372

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO
2nd rowNO
3rd rowNO
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
NO89095
93.3%
SI6372
 
6.7%

Length

2022-05-04T17:40:19.638890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-04T17:40:19.760535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
no89095
93.3%
si6372
 
6.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

imp_financ
Real number (ℝ≥0)

ZEROS

El dinero mensual que paga por los terminales financiados.

Distinct6373
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.486331419
Minimum0
Maximum39.99012758
Zeros89095
Zeros (%)93.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-05-04T17:40:19.954115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile13.45309146
Maximum39.99012758
Range39.99012758
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.148373198
Coefficient of variation (CV)4.136609857
Kurtosis19.32188231
Mean1.486331419
Median Absolute Deviation (MAD)0
Skewness4.431783189
Sum141895.6016
Variance37.80249299
MonotonicityNot monotonic
2022-05-04T17:40:20.265612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=80)
ValueCountFrequency (%)
089095
93.3%
26.581022481
 
< 0.1%
12.954189891
 
< 0.1%
28.941094721
 
< 0.1%
39.352871051
 
< 0.1%
29.237944821
 
< 0.1%
18.599532441
 
< 0.1%
26.950923461
 
< 0.1%
16.986238041
 
< 0.1%
5.8166860741
 
< 0.1%
Other values (6363)6363
 
6.7%
ValueCountFrequency (%)
089095
93.3%
5.0099986641
 
< 0.1%
5.0133093091
 
< 0.1%
5.0214175881
 
< 0.1%
5.0250748751
 
< 0.1%
ValueCountFrequency (%)
39.990127581
< 0.1%
39.988978141
< 0.1%
39.987564761
< 0.1%
39.978376011
< 0.1%
39.96236291
< 0.1%

descuentos
Categorical

SI = el cliente tiene activado algun descuento.

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
NO
76313 
SI
19154 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO
2nd rowSI
3rd rowSI
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
NO76313
79.9%
SI19154
 
20.1%

Length

2022-05-04T17:40:20.518757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-04T17:40:20.641347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
no76313
79.9%
si19154
 
20.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-05-04T17:40:07.546519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:44.914355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:47.705633image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:50.565722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:53.544310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:56.235763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:58.964443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:01.884704image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:04.721760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:07.848775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:45.242154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:48.021563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:50.861309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:53.829865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:56.523100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:59.290309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:02.198953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:05.038562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:08.146841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:45.561691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:48.342866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:51.191888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:54.132786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:56.829566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:59.619694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:02.515620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:05.356288image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:08.445521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:45.876221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:48.667552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:51.508477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:54.437630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:57.132293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:59.918770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:02.840053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:05.699448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:08.728010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:46.167948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:48.977078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:51.820498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:54.725411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:57.419000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:00.206229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:03.138219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:06.001961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:09.191282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:46.463614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:49.276251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:52.120510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:55.002521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:57.715145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:00.499330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:03.433594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:06.297172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:09.465657image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:46.773744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:49.579443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:52.427442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:55.299155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:58.002207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:00.790679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:03.744499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:06.596618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:09.774350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:47.083295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:49.908005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:52.738930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:55.612051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:58.305412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:01.105869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:04.079443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:06.922276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:10.088015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:47.386438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:50.240254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:53.050049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:55.921325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:39:58.623407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:01.590934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:04.399881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-04T17:40:07.241099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-05-04T17:40:20.785436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-04T17:40:21.120085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-04T17:40:21.452561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-04T17:40:21.803380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-05-04T17:40:22.170192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-05-04T17:40:10.629984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-04T17:40:11.392383image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-04T17:40:12.021533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

edadfacturacionantiguedadprovincianum_lineasnum_lineas_impagoincidencianum_llamad_entnum_llamad_salmb_datosseg_llamad_entseg_llamad_salconexionvel_conexionTVfinanciacionimp_financdescuentos
063216.0281092018-11-23 08:48:00La Rioja50.0NO11079108971280613751FIBRA50MBtv-futbolNO0.000000NO
184255.8308422017-08-22 03:19:00Vizcaya30.0NO1898918657649910862FIBRA600MBtv-futbolNO0.000000SI
266135.7681532001-12-27 13:50:00Albacete40.0NO12930155111701316743ADSL35MBtv-futbolNO0.000000SI
369255.6585272015-08-08 10:53:00Lugo40.0NO51521267033936771FIBRA200MBtv-familiarNO0.000000NO
43022.3028451997-08-29 02:19:00Tarragona22.0NO183323756184364485ADSL10MBtv-futbolNO0.000000NO
55199.3486451997-11-04 11:43:00Huelva40.0NO204511842889564764FIBRA200MBtv-futbolNO0.000000NO
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Last rows

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954587532.2974452008-12-02 03:40:00Tarragona20.0NO1875201931959514760FIBRA200MBtv-futbolNO0.000000NO
9545927228.4563402012-03-28 19:18:00Orense31.0NO149391757117503260ADSL200MBtv-familiarNO0.000000NO
9546058375.6584202016-06-09 21:39:00Santa Cruz de Tenerife50.0NO27428360176841997FIBRA100MBtv-totalNO0.000000NO
954613215.5706802013-01-18 12:54:00Tarragona20.0NO8578104061045118640FIBRA200MBtv-futbolNO0.000000SI
9546265173.7416672019-03-05 00:00:00Murcia50.0NO121981340361976853ADSL35MBtv-familiarSI23.138779NO
9546336215.8903262013-04-09 13:33:00Guadalajara30.0NO9813529136841667ADSL30MBtv-futbolNO0.000000NO
9546468285.8907502003-08-08 23:57:00Asturias50.0NO22620200025725679FIBRA200MBtv-futbolSI14.616422NO
9546520383.1676102013-03-27 20:07:00Álava40.0NO126261644883314398ADSL20MBtv-futbolNO0.000000NO
954661857.1589272009-10-22 19:17:00Las Palmas40.0NO852517933186172115ADSL25MBtv-familiarNO0.000000SI